predict_EA

<code>predict_EA</code> and <code>predict_DA</code> specify the predictive scheme to be used in the generation of the
predictions. EA refers to Earliest Antecedents nad DA to Direct Antecedents.

A powerful, easy to syntax for specifying and estimating complex
Structural Equation Models. Models can be estimated using Partial
Least Squares Path Modeling or Covariance-Based Structural Equation
Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).

Nicholas Danks

seminr

Building and Estimating Structural Equation Models

Soumya Ray

Nicholas Patrick Danks

André Calero Valdez

Juan Manuel Velasquez Estrada

James Uanhoro

Johannes Nakayama

Lilian Koyan

Laura Burbach

Arturo Heynar Cano Bejar

Susanne Adler

predict_EA function

<dl><dt>smMatrix</dt>
<dd>is the <code>structural_model</code> - a source-to-target matrix representing the inner/structural model,
generated by <code>relationships</code> generated by SEMinR.</dd>
<dt>path_coef</dt>
<dd>is the Path Coefficients matrix from a SEMinR model.</dd>
<dt>construct_scores</dt>
<dd>is the matrix of construct scores generated by SEMinR.</dd></dl>

Arguments

Predictive Scheme — predict_EA

<dl>

<dt>smMatrix</dt>
<dd>is the <code>structural_model</code> - a source-to-target matrix representing the inner/structural model,
generated by <code>relationships</code> generated by SEMinR.</dd>


<dt>path_coef</dt>
<dd>is the Path Coefficients matrix from a SEMinR model.</dd>


<dt>construct_scores</dt>
<dd>is the matrix of construct scores generated by SEMinR.</dd>

</dl>

Predictive Scheme

predict_EA: Predictive Scheme

Description

Usage

Arguments